class_docstr: Base ModelInterface for torch and tensorflow models.
class_docstr_tok: ["Base","ModelInterface","for","torch","and","tensorflow","models","."]
docstr: Predict on the given input.
  
  Parameters
  ----------
  x: np.ndarray
   A given input that the wrapped model predicts on.
  kwargs: optional
      Keyword arguments.
docstr_tok: ["Predict","on","the","given","input",".","Parameters","--","--","--","--","--","x",":","np.ndarray","A","given","input","that","the","wrapped","model","predicts","on",".","kwargs",":","optional","Keyword","arguments","."]
code: def predict(self, x: np.array, **kwargs):
      """
      Predict on the given input.
  
      Parameters
      ----------
      x: np.ndarray
       A given input that the wrapped model predicts on.
      kwargs: optional
          Keyword arguments.
      """
      raise NotImplementedError
  
file_path: Quantus/quantus/helpers/model/model_interface.py
imports: import warnings
  from importlib import util
  from abc import ABC, abstractmethod
  from typing import Any, Dict, Optional, Tuple, List, Union, Generator, TypeVar, Generic
  import numpy
label_desc: Use image node_id 3 for calling the ModelInterface obj's underlying member method code with example usage: obj.predict(x) without return types